Interpretability

Towards Understanding Subliminal Learning: When and How Hidden Biases Transfer
Do Sparse Autoencoders Generalize? A Case Study of Answerability
Scaling Sparse Feature Circuit Finding for In-Context Learning
Towards Interpreting Visual Information Processing in Vision-Language Models
Chain-of-Thought Is Not Explainability
Interpreting Learned Feedback Patterns in Large Language Models
Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders
Interpreting Context Look-ups in Transformers: Investigating Attention-MLP Interactions
Towards Interpretable Sequence Continuation: Analyzing Shared Circuits in Large Language Models
Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders